A normal mode back-propagation approach for broadband sound source - - PowerPoint PPT Presentation

A normal mode back-propagation approach for broadband sound source localization and the effects of water column variability

Ying-Tsong Lin, James F. Lynch and Timothy F. Duda Woods Hole Oceanographic Institution

Outline

• An acoustic normal mode back-propagation

approach for low-frequency broadband sound source localization in a shallow-water ocean

• Application to the New Jersey Shallow Water

2006 (SW06) experiment data

• Effects of water-column variability on source

range estimates

• I. Introduction - Acoustic normal mode theory
• Acoustic normal modes are orthogonal bases to decompose a sound

pressure field, and can describe the spatial field coherence.

Sound pulse propagation in a shallow-water mixed-layer waveguide model Source at 25m depth (fc = 50Hz, BW = 50Hz) Shallow-water low-frequency broadband sound propagation simulation

• I. Introduction - Acoustic normal mode theory
• Acoustic normal modes are orthogonal bases to decompose a sound

pressure field, and can describe the spatial field coherence. Modes are frequency-dependent!!

Shallow-water low-frequency broadband sound propagation simulation

• I. Introduction - Acoustic normal mode theory
• Acoustic normal modes are orthogonal bases to decompose a sound

pressure field, and can describe the spatial field coherence.

Sound pulse propagation in the mixed-layer waveguide model Source at 25m depth (fc = 50Hz, BW = 50Hz)

Modes are dispersive!!

Shallow-water low-frequency broadband sound propagation simulation

• I. Introduction - Acoustic normal mode theory
• Acoustic normal modes are orthogonal bases to decompose a sound

pressure field, and can describe the spatial field coherence. Modes are dispersive!!

Shallow-water low-frequency broadband sound propagation simulation

back propagate modes for 15 km

• II. Low-frequency broadband source localization by back-

propagating acoustic normal modes

• This method is theoretically straight forward ⎯ utilizing modal

dispersion to localize a sound source.

• The first step is to implement a vertical mode filter to obtain

individual modal arrivals. Then, back propagate the modal arrivals with their own speeds, which are derived from the waveguide

• parameters. The source range estimate is where the back-

propagated modes line up with each other.

back propagate modes for 5 km received signal

• III. Application to the New Jersey Shallow Water 2006

(SW06) experiment data

vertical line array (VLA)

64-m long, 16-element array covering the water column from 13 m depth to the bottom (79 m) 465-m long, 32-element array

horizontal line array (HLA) N

• III. Application to the New Jersey Shallow Water 2006 (SW06)

experiment data ⎯ U. Miami sound source (MSM) localization

• MSM source was 19.74 km northeast (25.73o due north) away from the

WHOI VLA.

• M-sequence phase encoded source signals. 5 different frequency bands.
• 100 Hz signal (25 Hz bandwidth) is considered here. Every ½ hour, a

1.5-min long transmission, which contained 36 identical M-sequence phase encoded signals, is emitted. Complex pulses are obtained from matched filter (pulse compression).

• III. Application to the New Jersey Shallow Water 2006 (SW06)

experiment data ⎯ U. Miami sound source (MSM) localization

• Least squares mode

filtering the VLA data

• Mode functions are

derived full water-column sound speed profile (SSP) measurements1 and a bottom geoacoustic model from a previous study2.

2 Y.-M. Jiang, N. R. Chapman and M. Badiey, “Quantifying the uncertainty of geoacoustic parameter estimates for the New

Jersey self by inverting air gun data,” J. Acoustic. Soc. Am. 121, 1879-1894 (2007).

1 Y.-T. Lin, A.E. Newhall, T.F. Duda, P.F.J. Lermusiaux and P.J. Haley Jr., “Statistical Merging of Data Sources to Estimate

Full Water-Column Sound Speed in the New Jersey Shallow Water 2006 Experiment,” submitted to IEEE JOE (2009).

• III. Application to the New Jersey Shallow Water 2006 (SW06)

experiment data ⎯ U. Miami sound source (MSM) localization

SSP measurement at VLA SSP measurement at ENV#32 (~9.6 km far) SSP measurement near MSM source (~20 km far)

tidal data 19.74 km sea surface sea floor bottom geoacoustic parameters ?

• Normal mode back-propagation

⎯ Assumptions: 2-D in-plane propagation and no mode-coupling.

⎯ Environmental reconstruction: 3 range-independent water-column SSP patches, accurate bathymetry and tidal data. ⎯ Normal modes are calculated every 150 m and back-propagated in a 25-m interval.

• III. Application to the New Jersey Shallow Water 2006 (SW06)

experiment data ⎯ U. Miami sound source (MSM) localization

• An example of normal mode back-propagation

• III. Application to the New Jersey Shallow Water 2006 (SW06)

experiment data ⎯ U. Miami sound source (MSM) localization

• Source localization results

⎯ 8 days data are processed. Every ½ hour, 35 M-sequence pulses are analyzed and 35 range estimates are obtained. The average value and standard deviation (STD) of these estimates are plotted. ⎯ The total mean range estimate is 19.74 km, the same as the true distance, along with STD 570 m. ⎯ Bottom geoacoustic model : homogeneous bottom with sound speed 1,700 m/s and density 1.8 g/cm3.

• IV. Effects of water-column variability

⎯ U. Miami sound source (MSM) localization

• Nonlinear internal waves

⎯ The peaks of the standard deviations correlate with nonlinear internal wave events exactly.

Nonlinear internal wave signal (vertical current speed at ENV#32 mooring)

• IV. Effects of water-column variability

⎯ U. Miami sound source (MSM) localization

• Nonlinear internal waves

distort the coherent structure of the sound field due to mode coupling and 3-D sound propagation effects1,2 (acoustic ducting, radiation, refraction and shadowing).

2pAO8 (3:05) Acoustic ducting, refracting, and shadowing by curved nonlinear internal waves in shallow water, J.F. Lynch, Y.­T. Lin, T.F. Duda, A.E. Newhall and G. Gawarkiewicz

1 J.F. Lynch, Y.-T. Lin, T.F. Duda and A.E. Newhall, “Acoustic Ducting, Shadowing, Refraction and Dispersion by Curved Non-Linear Internal

Waves in Shallow Water,” submitted to IEEE JOE (2009)

2 Y.-T. Lin, T.F. Duda and J.F. Lynch, “Acoustic mode radiation from the termination of a truncated nonlinear internal gravity wave duct in a

shallow ocean area,” submitted to JASA (2009)

Satellite SAR Image

• IV. Effects of water-column variability

⎯ U. Miami sound source (MSM) localization

• Mesoscale variability

⎯ SSP measurements separated by ~10 km from each other. ⎯ Spatial Nyquist sampling rate of the SSP measurements is 20 km. ⎯ The SSP measurements can not resolve water- column variability that has wavelength less than 20 km.

• IV. Effects of water-column variability

⎯ U. Miami sound source (MSM) localization

• MIT-MSEAS1 (HOPS) data assimilation ocean model

(water temperature at 30 m depth)

1 P. F. J. Lermusiaux, P. J. Haley, Jr., W. G. Leslie, O. Logoutov, A. R. Robinson, Real-time forecasts and re-analyses for the

Autonomous Wide Aperture Cluster for Surveillance (AWACS-06) exercise in the Middle Atlantic Bight Shelfbreak Front and Hudson Canyon region, http://mseas.mit.edu/archive/AWACS/index_AWACS.html, 2006.

Large domain simulation small domain nested simulation

• IV. Effects of water-column variability

⎯ U. Miami sound source (MSM) localization

• Low-pass filtering source range estimates (cutoff frequency: 4 cycles per day)

⎯ A large portion of range estimate deviations contains in the frequency band less than 4 cycle per day.

Application to the New Jersey Shallow Water 2006 (SW06) experiment data ⎯ Sei whale localization

5aAB7 (9:30) Sei whale localization and vocalization frequency sweep rate estimation during the New Jersey Shallow Water 2006 experiment, A.E. Newhall, Y.-T. Lin, J.F. Lynch, and M.F. Baumgartner, ASA Portland meeting 2009.

• Sei whale calls have a frequency

bandwidth from 40 to 120 Hz.

Received whale call mode 1 mode 2

Conclusions and future work

• A normal mode back-propagation approach for low-frequency

broadband sound source localization in a shallow-water ocean is applied to the SW06 data.

• Nonlinear internal waves is responsible for the range estimate

deviations in small time-scale (< 2 min).

• Insufficient mesoscale structure measurement (in terms of sound

speeds) also cause range estimate deviation in a larger time-scale (hours).

• The normal mode back-propagation approach has been applied for

localizing Sei whales presented in the SW06 experiment.

• Future work: careful examination of meso-scale oceanographic

variability and connection to acoustics. Comparison of different source localization approaches. Reduction of estimation uncertainty.